AI-Powered Property Tax Query Resolution Pilot Model for Smart City Using DeepQuery

1. Background & Objective

Bilaspur, one of the 100 Smart Cities selected under the National Smart Cities Mission, aimed to improve citizen-centric digital services—starting with property tax. Historically, property tax queries were managed via static websites and in-person counters, both of which failed to provide real-time, comprehensible, multilingual support to residents.

To address this, Bilaspur Smart City Ltd. partnered with Presear Softwares Pvt. Ltd. to deploy DeepQuery, a multilingual AI-powered assistant trained specifically for municipal property tax services.

Objective:

Enable 24×7 query resolution for property tax via WhatsApp and web.
Simplify legal provisions and policy explanations using natural language.
Increase tax compliance and citizen satisfaction using AI.

2. Dataset Preparation & Knowledgebase Design

The bot’s core intelligence was developed from statutory documents and real citizen pain points.

Key Sources:

Chhattisgarh Municipal Corporation Act – Part IV Chapter XI: Taxation
Property tax demand notices, rebate policies, penalty circulars
Citizen RTI responses and past FAQs from Bilaspur Municipal Corporation
Internal municipal SOPs and mutation workflows

Knowledgebase Highlights:

300+ bilingual Q&A pairs created manually
Covered intents like tax calculation, rebates, penalty, payment deadlines, receipts, mutation status
Structured into contextual categories for retrieval

Example Intent Coverage:

"How much tax do I owe this year?"
"क्या छूट मार्च के बाद मिलती है?"
"I paid online but didn’t get a receipt."

3. Language Support & NLP Tuning

Given Bilaspur’s linguistic diversity, DeepQuery was built with multilingual capability at its core.

Approach:

All content was translated into Hindi with contextual integrity.
Hinglish and misspelled inputs were normalized using phonetic matching.
Regional dialect queries were supported via synonym mapping (e.g., “bhugtan”, “jama”, “kar”).

Enhancements:

Used custom embeddings for semantic similarity in both Hindi and English
Integrated fallback keyword detection to handle out-of-scope queries gracefully
Voice-to-text (STT) and text-to-speech (TTS) modules were added for accessibility

4. Model Architecture & Training

DeepQuery used a hybrid Retrieval-Augmented Generation (RAG) approach, tuned for government document comprehension.

Architecture:

Embedding-based semantic search (using in-house vector database)
Transformer-based response generation tuned on government corpus
Custom logic for contextual grounding (e.g., ward-specific rules, due dates)

Training Loop:

Initial supervised Q&A-based fine-tuning
Weekly retraining with new queries from real usage
Feedback from BMC officials and citizens used to refine answer sets

5. Integration & Deployment

Platforms:

Embedded Web Widget on Bilaspur Smart City official portal

6. Results & Impact

Within 60 days of deployment:

25,000+ citizen queries handled
< 5 seconds average response time
92% satisfaction score
Significant reduction in load on municipal helplines and counters

Citizen Experience:

Queries answered in natural language
Voice support enabled for illiterate and elderly users
Proactive reminders about rebate expiry via WhatsApp

7. Challenges & Learnings

Challenge	Solution Implemented
Variability in ward-level rules	Ward-specific filters and rule mapping
Phonetic Hindi and hybrid input	Phoneme-matching and transliteration layer
Legacy record inconsistencies	Introduced fallback messaging with manual redirection
Data security and citizen identity	OTP login and request-based access to personal tax details

8. Alignment with Cities Innovation Exchange (CiX)

DeepQuery directly supports CiX goals of scalable, AI-powered urban solutions.

Demonstrates scalable use of LLMs in urban governance
Promotes inclusion via multilingual, multimodal access
Reduces human dependency for high-volume citizen services
Builds a replicable template for other urban local bodies in India

9. Way Forward

Expansion into other domains: water tax, building approvals, trade licenses
Automated multilingual reminders for tax deadlines
Integration with Digital Property Ledger for real-time mutation updates
Real-time grievance lodging and tracking within the same AI assistant

AI-Powered Property Tax Query Resolution Pilot Model for Smart City Using DeepQuery

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1. Background & Objective

2. Dataset Preparation & Knowledgebase Design

3. Language Support & NLP Tuning

4. Model Architecture & Training

5. Integration & Deployment

6. Results & Impact

7. Challenges & Learnings

8. Alignment with Cities Innovation Exchange (CiX)

9. Way Forward

Command Palette

Comments

Case Studies

Enhancing Mission90+ with DeepQuery’s AI-Driven Analytics

More from this blog

1. Background & Objective

2. Dataset Preparation & Knowledgebase Design

3. Language Support & NLP Tuning

4. Model Architecture & Training

5. Integration & Deployment

6. Results & Impact

7. Challenges & Learnings

8. Alignment with Cities Innovation Exchange (CiX)

9. Way Forward